Water being the most important for existence of life and hence there exists a need to monitor it on a regular bases with the rising need. In our experimentation setup efforts have been made to minimize the unknowns an...
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Good explanations are essential to efficiently learning introductory programming concepts [10]. To provide high-quality explanations at scale, numerous systems automate the process by tracing the execution of code [8,...
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Live 360° video can provide users with an immersive viewing and interactive experience, and end-to-end live delay is an important metric in quality of experience (QoE). The low latency and high bandwidth nature m...
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The emergence of Generative AI features in news applications may radically change news consumption and challenge journalistic practices. To explore the future potentials and risks of this understudied area, we created...
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Playing video games can empower players with disabilities by providing them opportunities for connection, achievement, and cultural participation. However, as they continue playing, they need to devise alternative way...
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Global variables lie at the root of many programmer complaints about computational notebooks. While programmers in other environments often address these barriers with function scopes, notebook programmers use functio...
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Cloud computing today is dominated by multi-server jobs. These are jobs that request multiple servers simultaneously and hold onto all of these servers for the duration of the job. Multi-server jobs add a lot of compl...
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Cloud computing today is dominated by multi-server jobs. These are jobs that request multiple servers simultaneously and hold onto all of these servers for the duration of the job. Multi-server jobs add a lot of complexity to the traditional one-server-per-job model: an arrival might not "fit" into the available servers and might have to queue, blocking later arrivals and leaving servers idle. From a queueing perspective, almost nothing is understood about multi-server job queueing systems;even understanding the exact stability region is a very hard problem. In this paper, we investigate a multi-server job queueing model under scaling regimes where the number of servers in the system grows. Specifically, we consider a system with multiple classes of jobs, where jobs from different classes can request different numbers of servers and have different service time distributions, and jobs are served in first-come-first-served order. The multi-server job model opens up new scaling regimes where both the number of servers that a job needs and the system load scale with the total number of servers. Within these scaling regimes, we derive the first results on stability, queueing probability, and the transient analysis of the number of jobs in the system for each class. In particular we derive sufficient conditions for zero queueing. Our analysis introduces a novel way of extracting information from the Lyapunov drift, which can be applicable to a broader scope of problems in queueing systems.
Speech-to-text technologies have been shown to improve text input efficiency and potentially lower the barriers to writing. Recent LLM-assisted dictation tools aim to support writing with speech by bridging the gaps b...
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Digital learning platforms offer flexibility and customization but place significant demands on attention and self-regulation, challenges that are especially pronounced for children with Attention Deficit Hyperactivit...
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Near-term quantum computers contain noisy devices, which makes it difficult to infer the correct answer even if a program is run for thousands of trials. On current machines, qubit measurements tend to be the most err...
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ISBN:
(纸本)9781450385572
Near-term quantum computers contain noisy devices, which makes it difficult to infer the correct answer even if a program is run for thousands of trials. On current machines, qubit measurements tend to be the most error-prone operations (with an average error-rate of 4%) and often limit the size of quantum programs that can be run reliably on these systems. As quantum programs create and manipulate correlated states, all the program qubits are measured in each trial and thus, the severity of measurement errors increases with the program size. The fidelity of quantum programs can be improved by reducing the number of measurement operations. We present JigSaw, a framework that reduces the impact of measurement errors by running a program in two modes. First, running the entire program and measuring all the qubits for half of the trials to produce a global (albeit noisy) histogram. Second, running additional copies of the program and measuring only a subset of qubits in each copy, for the remaining trials, to produce localized ( higher fidelity) histograms over the measured qubits. JigSaw then employs a Bayesian post-processing step, whereby the histograms produced by the subset measurements are used to update the global histogram. Our evaluations using three different IBM quantum computers with 27 and 65 qubits show that JigSaw improves the success rate on average by 3.6x and up-to 8.4x. Our analysis shows that the storage and time complexity of JigSaw scales linearly with the number of qubits and trials, making JigSaw applicable to programs with hundreds of qubits.
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